Article Figures & Data
Figures
- Figure 1.
Experimental Procedure. A, In the scanner participants were presented with 60 different activities and 60 different snack foods in written form, each presented for three seconds with the instruction to imagine how much pleasure they would derive from it. Each item was presented twice, resulting in 240 trials. B, After scanning, participants made 500 binary choices between the presented items followed by a rating task in which they rated each of the 120 items on a visual analog scale.
- Figure 2.
Frontal cortex masks. Anatomically defined gray matter mask including Brodman areas 9–13, 25, 32, 33, 46, and 47 used in this study for each subject.
- Figure 3.
Multivoxel pattern analysis. Figure illustrating the steps of the main multivoxel pattern analysis used in this study. A, Twelve feature sets of different time intervals were created for each participant. B, Recursive feature elimination (RFE) was used within each category for each feature set to obtain the 25% most informative voxels. C, The feature set with the highest within-category performance was used to perform across category predictions by training a model on one item category (e.g., snack foods) and using it to predict preferences over items of the other category (e.g., activities) and vice versa.
- Figure 4.
Estimation performance across reward categories. A, Scatterplots of observed against estimated subjective values for one exemplary subject (Subject 3) for snack foods predicted by a model trained on preferences over activities (a → s) and vice versa (s → a), and (B) correlation coefficients between observed and estimated subjective value for all eight subjects. Numbers identify the individual subjects; *p < 0.05, **p < 0.01, ***p < 0.001 based on permutation testing.
- Figure 5.
Correctly predicted binary choices, using the predicted subjective values. Bars show the percentage of correctly predicted binary choices depending on the distance between the estimated subjective values of two items. The horizontal line shows the accuracy expected by chance (50%).
- Figure 6.
Voxel-clusters carrying across-category information based on the weights of the support vector regression models (Talairach x, y, z = −7, 51, 39) overlaid onto an anatomical average.
- Figure 7.
Voxel-clusters resulting from univariate analysis (Talairach x, y, z = −7, 51, 30) overlaid onto an anatomical average.
- Figure 8.
Prediction performance across participants. Each boxplot shows the prediction accuracy for an SVR model trained on one subject, predicting the preferences for all 120 items for the other seven subjects. Boxplot on the far left shows distribution of all 56 correlations obtained in this way. Error bars in boxes show the median prediction performance.
Tables
X Y Z No. participants No. voxels Right anterior mPFC 1 61 0 5 231 Right dorsal mPFC 2 52 36 4 204 Right mPFC 5 55 24 3 219 ACC 1 5 29 21 4 162 ACC 2 −7 37 31 4 169 Left dorsal PFC −7 52 36 3 202 Left mPFC 1 −4 55 0 4 214 Left mPFC 2 −6 58 18 3 279 Left anterior PFC −20 61 13 3 195 Clusters are listed if they show an overlap for at least two participants and a cluster size of at least 162 anatomical (= 6 functional) voxels. No smoothing was applied to the data. Coordinates correspond to Talairach coordinates of the voxel with the highest overlap across subjects.
